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		<doi>10.1109/SIBGRA.2004.1352947</doi>
		<citationkey>OliveiraJúniorKapFreCarSab:2004:HaReMu</citationkey>
		<title>Handwritten Recognition with Multiple Classifiers for Restricted Lexicon</title>
		<format>On-line</format>
		<year>2004</year>
		<numberoffiles>1</numberoffiles>
		<size>275 KiB</size>
		<author>Oliveira Júnior, José Josemar,</author>
		<author>Kapp, Marcelo Nepomoceno,</author>
		<author>Freitas, Cinthia Obladen de Almendra,</author>
		<author>Carvalho, João Marques de,</author>
		<author>Sabourin, Robert,</author>
		<affiliation>Universidade Federal de Campina Grande, Coordenação de Pós-Graduação em Engenharia Elétrica, Caixa Postal 10105, 58109-970, Campina Grande, PB - Brazil,</affiliation>
		<affiliation>Pontíficia Universidade Católica do Paraná, R. Imaculada Conceição 1155, 80215-901, Curitiba, PR - Brazil,</affiliation>
		<affiliation>Ècole de Technologie Superieure, 1100 Rue Notre Dame Ouest, H3C 1K3, Montreal, QC - Canada,</affiliation>
		<editor>Araújo, Arnaldo de Albuquerque,</editor>
		<editor>Comba, João Luiz Dihl,</editor>
		<editor>Navazo, Isabel,</editor>
		<editor>Sousa, Antônio Augusto de,</editor>
		<conferencename>Brazilian Symposium on Computer Graphics and Image Processing, 17 (SIBGRAPI) - Ibero-American Symposium on Computer Graphics, 2 (SIACG)</conferencename>
		<conferencelocation>Curitiba, PR, Brazil</conferencelocation>
		<date>17-20 Oct. 2004</date>
		<publisher>IEEE Computer Society</publisher>
		<publisheraddress>Los Alamitos</publisheraddress>
		<booktitle>Proceedings</booktitle>
		<tertiarytype>Full Paper</tertiarytype>
		<transferableflag>1</transferableflag>
		<versiontype>finaldraft</versiontype>
		<keywords>pattern recognition, multiple classifiers, handwritten recognition.</keywords>
		<abstract>This paper prsents a multiple classifier system applied to the handwritten word recognition (HWR) problem. The goal is to analyse the influence of different global classifiers taken isolatedly as well as combined in a particular HWR task. The application proposed is the recognition of the Portuguese handwritten names of the months. The strategy takes advantage of the complementarity mechanisms of three different classifiers: Conventional Neural Network, Class-Modular Neural Network and Hidden Markov Models, yielding a multiple classifier that is more efficient than either individual technique. The recognition rates obtained vary from 75.9% using the stand alone HMM classifier to 96.0% considering the classifiers combination.</abstract>
		<language>en</language>
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		<usergroup>josemar administrator</usergroup>
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